The field of the invention is systems and methods for medical and molecular imaging. More particularly, the invention relates to systems and methods for motion correction in positron emission tomography (“PET”) imaging.
Positrons are positively charged electrons that are emitted by radionuclides that have been prepared using a cyclotron or other device. These are employed as radioactive tracers called “radiopharmaceuticals” by incorporating them into substances, such as glucose or carbon dioxide. The radiopharmaceuticals are administered to a patient and become involved in biochemical or physiological processes such as blood flow, fatty acid and glucose metabolism; and protein synthesis.
Some common clinical applications of PET imaging include oncology and cardiology for detecting and staging of cancer and cardiac diseases, as well as for monitoring treatment response. In a PET imaging scan, a radioactive tracer that emits positrons is administered to the body of a patient. The released positrons immediately annihilate, creating photon pairs with 511 keV energies that propagate in opposite directions from the annihilation point. The volume distribution and concentration of radioactive tracers in the body is determined based on the detection of radiation outside the patient.
Generally, a PET scanner includes one or more rings of detectors that encircle the patient and convert the energy of each 511 keV photon into a flash of light that is sensed by a radiation detector, such as a photomultiplier tube (“PMT”). Coincidence detection circuits connected to the radiation detectors record only those photons that are detected simultaneously by two detectors located on opposite sides of the patient. The number of such simultaneous events indicates the number of positron annihilations that occurred along a virtual line joining the two opposing detectors, called the line of response (“LOR”). An image indicative of the tissue concentration of the positron emitting radionuclide is created by determining the number of such annihilation events at each location within the field-of-view.
Motion artifacts commonly found in PET images are mainly due to irregular motion, as well as periodic internal motion from respiratory and cardiac activity. In particular, subject motion is generally difficult to avoid, due to the long scan durations (up to several minutes) necessary for PET imaging to be of clinical value, leading to image degradation, or blurring, and severe artifacts when motion has large amplitudes. On the other hand, physiological activity causes organs, such as heart muscle, lung, or abdominal organs, to change location, shape, or local tissue density, resulting in complex, non-rigid movement patterns. These effects limit the spatial resolution that can be achieved in PET imaging much more than physical factors, such as detector size, photon non-collinearity and positron range of travel. For instance, the physical factors affecting spatial resolution generally contribute to a deterioration of spatial resolution on the order of 1-3 mm, as compared to 5-15 mm due to organ motion from respiratory activity. Clinical situations when motion may become important include, for example, small perfusion defects present in the myocardium and small liver, or lung tumors, which generally are not detectable, or are much less visible, on images including motion artifacts. Therefore, absent motion corrections may result in different diagnostic outcomes.
Many approaches have been explored in the effort to correct motion artifacts. Depending on whether the motion is estimated from the acquired PET data or by other instrumentation, the approaches can be divided into two groups: auto-correction and assisted-correction. For the auto-correction techniques, the measured PET data are divided into temporal frames, or gates, and the motion is then estimated between temporal frames from the PET data. The estimated motion field can then be used to transform the reconstructed images or the sinograms of each temporal frame to a reference frame. The accuracy of motion estimation using this approach is limited by the noise of PET images, which increases as the data set is divided into temporal frames for a dynamic image sequence. Moreover, the fact that the motion estimation relies on the generation of images or sinograms limits its temporal resolution. Thus, such methods are not suitable when the activity distribution is fast changing or the object is fast moving. For example, cardiac imaging of rapid dynamic functions, such as myocardial blood flow, may not be possible using a gated approach due to the substantial noise associated with rejecting a large number of detected events in low count frames. The reconstruction algorithms of the assisted-correction approaches are similar to auto-correction techniques except that the motion information is instead measured using an instrument other than the PET camera, such as video/infrared cameras, and approaches with structured light.
Therefore, given at least the above, there is a need for systems and methods that correct for internal and external motion artifacts commonly present in PET imaging.